{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Progressive Dynamic Hurdles Toy Experiment",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DqizSiNU_xy",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_DNOG5nBVKW1",
        "colab_type": "text"
      },
      "source": [
        "# Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z13HkznoVNOy",
        "colab_type": "text"
      },
      "source": [
        "This is an implementation of the Progressive Dynamic Hurdles (PDH) algorithm that was presented in [the Evolved Transformer paper.](https://arxiv.org/abs/1901.11117) A toy problem is used to compare the algorithm to plain evolution."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKe-WyHpXdZR",
        "colab_type": "text"
      },
      "source": [
        "# Toy Search Space\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrY7m6z-Xnt2",
        "colab_type": "text"
      },
      "source": [
        "This is a toy problem meant to simulate the architecture search problem presented in the paper. It is adapted\n",
        "from the toy problem presented by [Real et al. (2018)](https://arxiv.org/abs/1802.01548) with code based off of [their Colab.](https://colab.research.google.com/github/google-research/google-research/blob/master/evolution/regularized_evolution_algorithm/regularized_evolution.ipynb#scrollTo=eizp75WbBwaB)\n",
        "\n",
        "A key difference here is that there is not a single function used to compute all fitnesses. Instead there are two fitness functions, depending on how many steps a *Model* is allowed to \"train\" for:\n",
        "\n",
        "1) *get_final_accuracy*(): simulates the fitness computation of a model that has been trained for a\n",
        "hypothetical maximum number of train steps.\n",
        "\n",
        "2) *get_early_accuracy*(): simulates the fitness computation of a model that has been trained for 1/100th\n",
        "the hypothetical maximum number of train steps.\n",
        "\n",
        "Having two different fitnesses for two different number of train steps is necessary to apply the PDH algorithm. In line with this, each *Model* also has two accuracies: an *observed_accuracy*, which is what we observe when the model is evaluated during the search, and a *true_accuracy*, which is the accuracy the model achieves when it is trained for the maximum number of train steps.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNoAya1QNrTP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DIM = 100  # Number of bits in the bit strings (i.e. the \"models\").\n",
        "NOISE_STDEV = 0.01  # Standard deviation of the simulated training noise.\n",
        "EARLY_SIGNAL_NOISE = 0.005  # Standard deviation of the noise added to earlier\n",
        "                            # observations.\n",
        "REDUCTION_FACTOR = 100.0  # The factor by which the number of train steps is\n",
        "                          # reduced for earlier observations.\n",
        "\n",
        "class Model(object):\n",
        "  \"\"\"A class representing a model.\n",
        "\n",
        "  Attributes:\n",
        "    arch: the architecture as an int representing a bit-string of length `DIM`.\n",
        "        As a result, the integers are required to be less than `2**DIM`. \n",
        "    observed_accuracy: the simulated validation accuracy observed for the model\n",
        "        during the search. This may be either the accuracy after training for\n",
        "        the maximum number of steps or the accuracy after training for 1/100 the\n",
        "        maximum number of steps.\n",
        "    true_accuracy: the simulated validation accuracy after the maximum train\n",
        "        steps.\n",
        "  \"\"\"\n",
        "  def __init__(self):\n",
        "    self.arch = None\n",
        "    self.observed_accuracy = None\n",
        "    self.true_accuracy = None\n",
        "\n",
        "  \n",
        "def get_final_accuracy(arch):\n",
        "  \"\"\"Simulates training for the maximum number of steps and then evaluating.\n",
        "  \n",
        "  Args:\n",
        "    arch: the architecture as an int representing a bit-string.\n",
        "  \"\"\"\n",
        "  accuracy =  float(_sum_bits(arch)) / float(DIM)\n",
        "  accuracy += random.gauss(mu=0.0, sigma=NOISE_STDEV)\n",
        "  accuracy = 0.0 if accuracy < 0.0 else accuracy\n",
        "  accuracy = 1.0 if accuracy > 1.0 else accuracy\n",
        "  return accuracy\n",
        "          \n",
        "  \n",
        "def get_early_accuracy(final_accuracy):\n",
        "  \"\"\"Simulates training for 1/100 the maximum steps and then evaluating.\n",
        "  \n",
        "  Args:\n",
        "    final_accuracy: the accuracy of the model if trained for the maximum number\n",
        "        of steps.\n",
        "  \"\"\"\n",
        "  observed_accuracy = final_accuracy/REDUCTION_FACTOR + random.gauss(mu=0,\n",
        "      sigma=EARLY_SIGNAL_NOISE)\n",
        "  observed_accuracy = 0.0 if observed_accuracy < 0.0 else observed_accuracy\n",
        "  observed_accuracy = 1.0 if observed_accuracy > 1.0 else observed_accuracy\n",
        "  return observed_accuracy\n",
        "           \n",
        "\n",
        "def _sum_bits(arch):\n",
        "  \"\"\"Returns the number of 1s in the bit string.\n",
        "  \n",
        "  Args:\n",
        "    arch: an int representing the bit string.\n",
        "  \"\"\"\n",
        "  total = 0\n",
        "  for _ in range(DIM):\n",
        "    total += arch & 1\n",
        "    arch = (arch >> 1)\n",
        "  return total"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Plg2ZjlAOalx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "\n",
        "def random_architecture():\n",
        "  \"\"\"Returns a random architecture (bit-string) represented as an int.\"\"\"\n",
        "  return random.randint(0, 2**DIM - 1)\n",
        "\n",
        "def mutate_arch(parent_arch):\n",
        "  \"\"\"Computes the architecture for a child of the given parent architecture.\n",
        "\n",
        "  Args:\n",
        "    parent_arch: an int representing the architecture (bit-string) of the\n",
        "        parent.\n",
        "\n",
        "  Returns:\n",
        "    An int representing the architecture (bit-string) of the child.\n",
        "  \"\"\"\n",
        "  position = random.randint(0, DIM - 1)  # Index of the bit to flip.\n",
        "  \n",
        "  # Flip the bit at position `position` in `child_arch`.\n",
        "  child_arch = parent_arch ^ (1 << position)\n",
        "  \n",
        "  return child_arch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NnuffOaj-sA",
        "colab_type": "text"
      },
      "source": [
        "# Search Algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2I6MLkxOkhoS",
        "colab_type": "text"
      },
      "source": [
        "Here we implement both plain evolution and evolution + Progressive Dynamic Hurdles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RSAAAsWPAsf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import collections\n",
        "import random\n",
        "import copy\n",
        "\n",
        "\n",
        "def plain_evolution(cycles, population_size, sample_size, early_observation):\n",
        "  \"\"\"Plain evolution.\n",
        "  \n",
        "  Args:\n",
        "    cycles: the number of cycles the search is run for.\n",
        "    population_size: the size of the population.\n",
        "    sample_size: the size of the sample for both parent selection and killing.\n",
        "    early_observation: boolean. Whether or not we are observing the models early\n",
        "        by evaluating them for 1/100th the maximum number of train steps.\n",
        "  \"\"\"\n",
        "  population = collections.deque()\n",
        "  history = []  # Not used by the algorithm, only used to report results.\n",
        "\n",
        "  # Initialize the population with random models.\n",
        "  while len(population) < population_size:\n",
        "    model = Model()\n",
        "    model.arch = random_architecture()\n",
        "    model.true_accuracy = get_final_accuracy(model.arch)\n",
        "    # If we are observing early, get the early accuracy that corresponds to the\n",
        "    # true_accuracy. Else, we are training each model for the maximum number of\n",
        "    # steps and so the observed_accuracy is the true_accuracy.\n",
        "    if early_observation:\n",
        "      model.observed_accuracy = get_early_accuracy(model.true_accuracy)\n",
        "    else:\n",
        "      model.observed_accuracy = model.true_accuracy\n",
        "    population.append(model)\n",
        "    history.append(model)\n",
        "\n",
        "  # Carry out evolution in cycles. Each cycle produces a model and removes\n",
        "  # another.\n",
        "  while len(history) < cycles:\n",
        "    # Sample randomly chosen models from the current population.\n",
        "    sample = random.sample(population, sample_size)\n",
        "\n",
        "    # The parent is the best model in the samples, according to their observed\n",
        "    # accuracy.\n",
        "    parent = max(sample, key=lambda i: i.observed_accuracy)\n",
        "\n",
        "    # Create the child model and store it.\n",
        "    child = Model()\n",
        "    child.arch = mutate_arch(parent.arch)\n",
        "    child.true_accuracy = get_final_accuracy(child.arch)\n",
        "    # If we are observing early, get the early accuracy that corresponds to the\n",
        "    # true_accuracy. Else, we are training each model for the maximum number of\n",
        "    # steps and so the observed_accuracy is the true_accuracy.\n",
        "    if early_observation:\n",
        "      child.observed_accuracy = get_early_accuracy(child.true_accuracy)\n",
        "    else:\n",
        "      child.observed_accuracy = child.true_accuracy\n",
        "    \n",
        "    # Choose model to kill.\n",
        "    sample_indexes = random.sample(range(len(population)), sample_size)\n",
        "    min_fitness = float(\"inf\")\n",
        "    kill_index = population_size\n",
        "    for sample_index in sample_indexes:\n",
        "      if population[sample_index].observed_accuracy < min_fitness:\n",
        "        min_fitness = population[sample_index].observed_accuracy\n",
        "        kill_index = sample_index\n",
        "\n",
        "    # Replace victim with child.\n",
        "    population[kill_index] = child\n",
        "    \n",
        "    history.append(child)\n",
        "\n",
        "  return history, population\n",
        "\n",
        "\n",
        "def pdh_evolution(train_resources, population_size, sample_size):\n",
        "  \"\"\"Evolution with PDH.\n",
        "  \n",
        "  Args:\n",
        "    train_resources: the resources alotted for training. An early obsevation\n",
        "        costs 1, while a maximum train step observation costs 100.\n",
        "    population_size: the size of the population.\n",
        "    sample_size: the size of the sample for both parent selection and killing.\n",
        "    \"\"\"\n",
        "  population = collections.deque()\n",
        "  history = []  # Not used by the algorithm, only used to report results.\n",
        "  resources_used = 0  # The number of resource units used.\n",
        "  \n",
        "  # Initialize the population with random models.\n",
        "  while len(population) < population_size:\n",
        "    model = Model()\n",
        "    model.arch = random_architecture()\n",
        "    model.true_accuracy = get_final_accuracy(model.arch)\n",
        "    # Always initialize with the early observation, since no hurdle has been\n",
        "    # established.\n",
        "    model.observed_accuracy = get_early_accuracy(model.true_accuracy)\n",
        "    population.append(model)\n",
        "    history.append(model)\n",
        "    # Since we are only performing an early observation, we are only consuming\n",
        "    # 1 resource unit.\n",
        "    resources_used += 1\n",
        "\n",
        "  # Carry out evolution in cycles. Each cycle produces a model and removes\n",
        "  # another.\n",
        "  hurdle = None\n",
        "  while resources_used < train_resources:\n",
        "    # Sample randomly chosen models from the current population.\n",
        "    sample = random.sample(population, sample_size)\n",
        "\n",
        "    # The parent is the best model in the sample, according to the observed\n",
        "    # accuracy.\n",
        "    parent = max(sample, key=lambda i: i.observed_accuracy)\n",
        "\n",
        "    # Create the child model and store it.\n",
        "    child = Model()\n",
        "    child.arch = mutate_arch(parent.arch)\n",
        "    child.true_accuracy = get_final_accuracy(child.arch)\n",
        "    # Once the hurdle has been established, a model is trained for the maximum\n",
        "    # amount of train steps if it overcomes the hurdle value. Otherwise, it\n",
        "    # only trains for the lesser amount of train steps.\n",
        "    if hurdle:\n",
        "      child.observed_accuracy = get_early_accuracy(child.true_accuracy)\n",
        "      \n",
        "      # Performing the early observation costs 1 resource unit.\n",
        "      resources_used += 1\n",
        "      if child.observed_accuracy > hurdle:\n",
        "        child.observed_accuracy = child.true_accuracy\n",
        "        # Now that the model has trained longer, we consume additional\n",
        "        # resource units.\n",
        "        resources_used += REDUCTION_FACTOR - 1\n",
        "    else:\n",
        "      child.observed_accuracy = get_early_accuracy(child.true_accuracy)\n",
        "      # Since we are only performing an early observation, we are only consuming\n",
        "      # 1 resource unit.\n",
        "      resources_used += 1\n",
        "\n",
        "    # Choose model to kill.\n",
        "    sample_indexes = random.sample(range(len(population)), sample_size)\n",
        "    min_fitness = float(\"inf\")\n",
        "    kill_index = population_size\n",
        "    for sample_index in sample_indexes:\n",
        "      if population[sample_index].observed_accuracy < min_fitness:\n",
        "        min_fitness = population[sample_index].observed_accuracy\n",
        "        kill_index = sample_index\n",
        "\n",
        "    # Replace victim with child.\n",
        "    population[kill_index] = child\n",
        "    \n",
        "    history.append(child)\n",
        "    \n",
        "    # Create a hurdle, splitting resources such that the number of models\n",
        "    # trained before and after the hurdle are approximately even. Here, our\n",
        "    # appoximation is assuming that every model after the hurdle trains for the\n",
        "    # maximum number of steps.\n",
        "    if not hurdle and resources_used >= int(train_resources/REDUCTION_FACTOR):\n",
        "      hurdle = 0\n",
        "      for model in population:\n",
        "        hurdle += model.observed_accuracy\n",
        "      hurdle /= len(population)\n",
        "\n",
        "  return history, population"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dW06r4GyorUj",
        "colab_type": "text"
      },
      "source": [
        "# Experiments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eig8o4a4OioY",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "import seaborn as sns\n",
        "\n",
        "TOTAL_RESOURCES = 10000  # Total number of resource units.\n",
        "POPULATION_SIZE = 100  # The size of the population.\n",
        "SAMPLE_SIZE = 10  # The size the subpopulation used for selecting parents and\n",
        "                  # kill targets.\n",
        "\n",
        "def graph_values(values, title, xlim, ylim):\n",
        "  plt.figure()\n",
        "  sns.set_style('white')\n",
        "  xvalues = range(len(values))\n",
        "  yvalues = values\n",
        "  ax = plt.gca()\n",
        "  dot_size = int(TOTAL_RESOURCES / xlim)\n",
        "  ax.scatter(\n",
        "      xvalues, yvalues, marker='.', facecolor=(0.0, 0.0, 0.0),\n",
        "      edgecolor=(0.0, 0.0, 0.0), linewidth=1, s=dot_size)\n",
        "  ax.xaxis.set_major_locator(ticker.LinearLocator(numticks=2))\n",
        "  ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
        "  ax.yaxis.set_major_locator(ticker.LinearLocator(numticks=2))\n",
        "  ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
        "  ax.set_title(title, fontsize=20)\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(8, 6)\n",
        "  fig.tight_layout()\n",
        "  ax.tick_params(\n",
        "      axis='x', which='both', bottom=True, top=False, labelbottom=True,\n",
        "      labeltop=False, labelsize=14, pad=10)\n",
        "  ax.tick_params(\n",
        "      axis='y', which='both', left=True, right=False, labelleft=True,\n",
        "      labelright=False, labelsize=14, pad=5)\n",
        "  plt.xlabel('Number of Models Evaluated', labelpad=-16, fontsize=16)\n",
        "  plt.ylabel('Accuracy', labelpad=-30, fontsize=16)\n",
        "  plt.xlim(0, xlim + .05)\n",
        "  plt.ylim(0, ylim + .05)\n",
        "  sns.despine()\n",
        "  \n",
        "def graph_history(history):\n",
        "  observed_accuracies = [i.observed_accuracy for i in history]\n",
        "  true_accuracies = [i.true_accuracy for i in history]\n",
        "  graph_values(observed_accuracies, \"Observed Accuracy\",\n",
        "              xlim=len(history), ylim=max(observed_accuracies))\n",
        "  graph_values(true_accuracies, \"True Accuracy\",\n",
        "              xlim=len(history), ylim=max(true_accuracies))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri7c6KiwvM2z",
        "colab_type": "text"
      },
      "source": [
        "**Plain Evolution: Early Observartion**\n",
        "\n",
        "First, we run plain evolution with early observations. We get to observe many models (10K), but, while still useful, the accuracy signal is noisy, hurting the performance of the search.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SGTmMpaO8z3",
        "colab_type": "code",
        "outputId": "6d77338f-d944-4716-9664-1d0a9da37441",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "history, _ = plain_evolution(\n",
        "    cycles=TOTAL_RESOURCES, population_size=POPULATION_SIZE,\n",
        "    sample_size=SAMPLE_SIZE,\n",
        "    early_observation=True)\n",
        "\n",
        "graph_history(history)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yXnkyPPWj+1Ou2laq04GpTLHYhKAX3bE/dUWZgnJKg6a7ly8VIJB8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\niZH/DydmNCSnltlfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UXCKRcOl0OjKHLZDhNeZeLpfTeykVsCwvL6v/89nOhYUog7ceq7lZAGjNXpe9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/OruyhERERUAwV9cHi7du2wb9++YG8mIOLi4rBixQrExcVVd1GIiIioBgp64DR+/HhkZmbi888/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\n/w8lnT0r5j94TwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa7OtnOnvujQ",
        "colab_type": "text"
      },
      "source": [
        "**Plain Evolution: Maximum Step Observartion**\n",
        "\n",
        "Next, we run plain evolution with observations after each model has been trained for the maximum number\n",
        "of steps. The signal here is perfect, with the observed accuracy matching the true accuracy, but we see very few models since we are controlling for number of resources.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2cmdc26xYtS",
        "colab_type": "code",
        "outputId": "32be64e0-73fd-4af7-9d3e-6c06c34ed879",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "history, _ = plain_evolution(\n",
        "    cycles=TOTAL_RESOURCES/REDUCTION_FACTOR, population_size=POPULATION_SIZE,\n",
        "    sample_size=SAMPLE_SIZE, early_observation=False)\n",
        "\n",
        "graph_history(history)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RBZ13R8ioUaPUtWtXj6TskliVjsLxpaYH9jfn3HU8Wb58uRISEjRu3DglJCT4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\nSZKUa8aQJEnKNWNIkiTl2itmDE41F98DRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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69tprlZmZyUkCPovnE3pOeeeMVatWae3atR4/p3kCzWRV5E1XNb4y3o+3ccfn\nYq9vUl5enuLi4tSpUycCKrxeVS8KGVywcqrz4tveOeP7779X165dTXsR5vNh6ODBg0pLS6v2k4k3\nDVrHQaXmKq9vUqdOnQio8HoMzeEe1X3xXd45Y8eOHSooKJBkvoFYff5usgMHDrhksCZvGrTO3b3u\nUX0YYA01mTN3Prpi3/fV5+1V9x2m9s4ZAQEBKiwstJlmpoFYfb5mSHJNwvWmqxp397pH9WGANdRk\nztRKV/e+701dF6pbddf+2ztn2Ltxw0wX1aYIQ1L1Nxt5UwDhhFqz0WcLNZU3Dc3hTV0Xqlt1X3xX\nZtR9s1xU+/zdZNdcc4327NnjkrsUuAsLgJl5U21MYmKipkyZovNPaRaLRQkJCZo4caJby1Ld3DnU\nh1nPaaYIQzk5OT5TXQoA3sRbTqC+fqu+t3zOvsrnw1D37t31wgsvsOMAgA/zploq1Dw+H4bMMgI1\nAJgdtSeoKtN0oAZQNd4y0jpQEW5GQFURhgCUi2H7AZgBYQjwMQzbDwCVQxgCfAjD9gNA5fn84zgA\nM2HYfgCoPMKQF/HV5+rAfar7mXn2nh/Vtm1bnoUHwKfQTOYlGCMD1YFh+wGg8hhnyEv4+uipcA+G\n7QeAyqNmyEtU91OJYU7uemgv47kA8CWEIS9R3c0b3oaB+9yHoAIAlUMY8hKlHVV9sR8G/aEAAN6M\nMOQl3NW84Qn2Bu5jXBoAgLcgDHkRX23eoD8UAMCbMc4QXM7ewH2+1B8KAFCzUTMEl/Pl/lC4ODrO\noxT7ArwZYQgu58v9oVA+Os6jFPsCvB1hCG7haH8orh59Bx3nUYp9Ad6OMASvUd7V46pVq7R27VoC\nUg1Dx3mUYl+AtyMMwWvYu3r8/vvv1bVrV2VmZlK9XsP4+kCicBz7Arwdd5PBa5R39bhjxw6dPn1a\nhmHYVK/Du9l74j0d582JfQHejpoheA17V48BAQEqLCy0mY/q9ZqBjvMoxb4Ab8dT6+E17PUZat26\ntTIzM20CUlBQkJKSkghDAIBqQc0QvIa9q8d+/fpp4MCBjFEEAHAZaobg9Upvt6d6HQDgCtQMwev5\n6jPbAADegbvJAACAqRGGAACAqRGGAACAqbk8DPl4/2wAAFDDuTwM9e7dW7Nnz9bhw4ddvSoAAIBK\nc3kY6tatm+bPn6++ffvq0Ucf1caNG129SgAAAIe5ZZyhU6dOafny5froo4+0c+dOtWjRQnfccYdu\nu+02NWzY0KXrZpwhAABwMW4fdHHbtm1KTk7WmjVrZBiGbrrpJg0fPlyRkZEuWR9hCAAAXIzb7ybr\n1KmToqOj1bZtWxUWFmr9+vX4ndSVAAAgAElEQVQaNWqUbrvtNmVmZrq7OAAAwOTcFoYOHjyot99+\nW7169dLjjz+uevXqac6cOfrxxx+1YMECnT17Vs8++6y7igMAACDJDY/j+PLLL5WcnKyNGzcqKChI\ncXFxuvPOO3XVVVdZ5+nRo4cmTJig+Ph4VxcHAADAhsvD0NixY9W+fXtNnTpVN998swICAuzOd/XV\nV2vw4MGuLg4AAIANl3eg/uWXX9SuXTtXruKi6EANAAAuxuV9hpo1a6Zdu3bZfW3Xrl3KyclxdREA\nAADK5fIw9OKLL2rRokV2X/vXv/6ll156ydVFAAAAKJfLw9CPP/6onj172n2tZ8+e+vHHH11dBAAA\ngHK5PAydOHFC9erVs/taUFCQjh8/7uoiAAAAlMvlYeiKK67QTz/9ZPe1n376ScHBwa4uAgAAQLlc\nHoZiYmI0d+5cffXVVzbTv/rqK82bN08DBgxwdREAAADK5fJxhh555BFt27ZNY8aMUePGjdW0aVMd\nPnxY2dnZ6tixox599FFXFwEAAKBcLg9Dl156qRYvXqzU1FRt2rRJx48f1zXXXKMePXpoyJAhuuQS\nlxcBAICLKi4uVnp6ujIyMhQREaEBAwbI39/f08WCm7j9qfXuxqCLAICLKS4uVkxMjDZv3qzc3FwF\nBgYqMjJSa9asIRCZhNufWg8AgDdJT0/X5s2bdfr0aRmGodOnT2vz5s1KT0/3dNHgJm5po9q4caOS\nkpK0a9cunT171uY1i8Wizz//3B3FAACgjIyMDOXm5tpMy83N1fbt2zVo0CAPlQru5PKaoa+//lqj\nR49Wfn6+/vjjD7Vq1UpXXnmlDh06JD8/P3Xp0sXVRQAAoFwREREKDAy0mRYYGKjw8HAPlQju5vIw\nNGfOHN11112aN2+eJOnxxx/X4sWLlZaWpuLiYkVFRbm6CAAAlGvAgAGKjIxUUFCQLBaLgoKCFBkZ\nydAvJuLyZrI//vhDjz32mPz8/GSxWFRcXCxJatmypcaNG6d33nlHAwcOdHUxAACwy9/fX2vWrFF6\nerq2b9+u8PBw7iYzGZeHIT8/P/n7+8tisahhw4Y6cOCAOnToIElq0qSJ9uzZ4+oiAABwUf7+/ho0\naBB9hEzK5c1kLVu21P79+yVJYWFhev/995WVlaWcnBwtXLhQzZs3d3URAAAAyuXymqHBgwcrMzNT\nkjRu3Djdd999uvHGGyWdS+Kvv/66q4sAAABQLrcPunjo0CFt2LBBZ86c0d/+9jdde+21Ll0fgy4C\nAICLcWnNUEFBgZKSktS9e3eFhIRIOvcU+9tvv92VqwUAAHCYS/sMBQQE6I033tCJEydcuRoAAIAq\nc3kH6tatW2vv3r2uXg0AAECVuDwMPfbYY5ozZ45+++03V68KAACg0lx+N9n8+fOVl5en2NhYNW/e\nXMHBwbJYLNbXLRaLlixZ4upiAAAA2OXyMOTv76/WrVu7ejUAAABV4vIwtHjxYlevAgAAoMpc3mcI\nAADAm7m8Zmjr1q0VztOlSxdXFwMAAMAul4ehkSNH2nSYtmfHjh2uLgYAAIBdLg9DH3zwQZlpx48f\n1/r167V161ZNmjTJ1UUAAAAol8vDUNeuXe1O79evn6ZNm6b169dbH9wKAADgbh7tQN2rVy+lp6d7\nsggAAMDkPBqGdu3aJT8/bmgDAACe4/JmshUrVpSZVlhYqN9//13Lli1Tv379XF0EAACAcrk8DE2Y\nMMHu9ICAAA0cOFAvvPCCq4sAAABQLpeHoS+++KLMtNq1a6tx48auXjUAAECFXB6Gmjdv7upVAAAA\nVJnLey+vX7++3KfSL126VF9//bWriwAAAFAul4ehOXPmKC8vz+5r+fn5mjNnjquLAAAAUC6Xh6E/\n/vhD7dq1s/ta27ZtlZmZ6eoiAAAAlMvlYaikpKTcmqHc3FwVFRW5uggAAADlcnkYatOmjVauXGn3\ntZUrVyo0NNTVRQAAACiXy8PQ/fffr7Vr1+qxxx7Txo0btXPnTn377bd67LHHtG7dOj3wwAOuLgIA\nAEC5XH5rfXR0tF544QW99dZbWrdunSTJMAzVrVtXEydOZARqAADgUS4PQ5I0cuRIxcbGKiMjQ8eP\nH1eDBg0UERGhwMBAd6weAACgXG4JQ5IUFBSkqKgod60OAADAIS7vMzRv3jwlJibafW3q1KlasGCB\nq4sAAABQLpeHoZSUlHLvGGvTpo1SUlJcXQQAAIByuTwMHTx4UNdcc43d16666iodOHDA1UUAAAAo\nl8vDUJ06dXT48GG7rx06dEgBAQGuLgIAAEC5XB6Grr/+er333nsqKCiwmV5QUKBFixapc+fOri4C\nAABAuVx+N9m4ceM0fPhwxcTEaMiQIWrSpImysrL06aef6vjx45o+fbqriwAAAFAul4ehNm3a6IMP\nPtA//vEPzZ8/XyUlJfLz81Pnzp01Y8YMtWnTxtVFAAAAKJfFMAzDXSvLz8/XiRMndPnll6tOnTra\nsmWLli9frldeecVl64yLi+OONQAAUC6X9xk6X506dZSfn6+5c+eqT58+uueee7R69Wp3FgEAAMCG\nW0agPnXqlFatWqXly5frp59+knSu+eyhhx7SoEGD3FEEAAAAu1wWhkpKSrRhwwYtX75c69ev19mz\nZ9WkSRPdddddWrp0qZ5//nl16dLFVasHAABwiEvC0PTp05WWlqajR4+qdu3auummmxQbG6u//e1v\nOn36tJYsWeKK1QIAAFSaS8LQv/71L1ksFt1444165ZVX1KBBA+trFovFFasEAACoEpd0oL7tttsU\nGBior776Sv3791dCQoL+85//uGJVAAAATnFJzdDUqVM1adIkrVu3TsuXL1dycrKSkpL0l7/8RdHR\n0dQOAQAAr+GWcYaysrKUmpqq1NRU7dy5U5IUHh6uESNGqH///qpdu7bL1s04QwAA4GLcOuiiJP3f\n//2fVqxYoc8++0zHjx9XvXr1tHXrVpetjzAEAAAuxi3jDJ2vffv2at++vSZMmKCvvvpKK1ascHcR\nAAAArNwehkrVqlVL0dHRio6O9lQRAAAA3Ps4DgAAAG9DGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIA\nAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZG\nGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKZGGAIAAKbmcBhaunSp+vTpo/bt2ysuLk7btm276PxbtmxR\nXFyc2rdvr759+yopKanSyywoKFBiYqIiIyMVHh6uhx9+WIcOHXK0yAAAABVyKAytWrVK06ZN08MP\nP6wVK1YoIiJCo0eP1oEDB+zOv3fvXj300EOKiIjQihUrFB8fr6lTp2rNmjWVWubLL7+sNWvW6M03\n39TSpUuVm5ur+Ph4FRcXO7nZAAAA5zgUhhYtWqTY2Fjdcccdat26tSZNmqTg4GC7tT2S9OGHH6pJ\nkyaaNGmSWrdurTvuuENDhw7VwoULHV7mqVOn9Mknn2j8+PHq0aOH2rVrp1dffVW//fabNm3aVA2b\nDgAA4EAYKigo0C+//KIePXrYTO/Ro4cyMjLsvmf79u1l5u/Zs6d+/vlnFRYWOrTM0nl79uxpfb1Z\ns2Zq3bp1uesFAACorEsqmuHYsWMqLi5W48aNbaY3atSo3Bqa7Oxsde/e3WZa48aNVVRUpGPHjskw\njAqXmZ2dLX9/fzVo0KDMPNnZ2Rctc3JyspKTkyVJv//+u+Li4iraTAAAUEOcPXtWn332WbUtr8Iw\nVBMNGzZMw4YNkyTFxcUpJSXFwyUCAADVpborOSpsJmvQoIH8/f3L1MYcPXpUwcHBdt/TuHFjHT16\n1GZadna2LrnkEjVo0MChZTZu3FjFxcU6duxYmXkurFECAACoqgrDUEBAgNq1a1emSWzTpk2KiIiw\n+57w8HC784eFhalWrVoOLbN03m+//db6+qFDh5SZmVnuegEAACrL/8UXX3yxopmCgoI0c+ZMBQcH\nq06dOpozZ462bdumadOm6bLLLtP48eO1bt06RUdHS5KuvvpqzZ8/X0ePHlXz5s31xRdf6N1339WE\nCRN07bXXOrTM2rVr6/Dhw1q6dKlCQ0N16tQpTZ48WfXq1dPTTz8tPz/Hx4sMCwur2qcDAAC8UnWe\n2y2GYRiOzLh06VK99957ysrKUkhIiJ577jl16dJFkjRy5EhJ0uLFi63zb9myRa+88or++9//qkmT\nJho9erRGjBjh8DKlc3ey/eMf/1BaWpry8/PVvXt3TZkyRc2aNXN6wwEAAKRKhCEAAABfxLPJAACA\nqRGGAACAqRGGAACAqRGGAACAqRGGAACAqRGGYGopKSkKDQ3V9ddfrxMnTti8VlRUpNDQUM2cOdPt\n5Zo5c6ZCQ0NVVFTk9nVXRklJiV5++WX17NlTbdq00dixY8udt0+fPgoNDdVTTz1l9/WRI0cqNDS0\nzBAczpgwYYL69OlT6fdt3rxZoaGh2rx5s1PrL11OeX8nT550avnl6dOnjyZMmOCSZUvSjh07NHPm\nTB0/frxal1v6e9y3b1+1LheoiE8+mwyorFOnTmn+/Pl6+umnPV2UGmX16tX64IMPNGHCBIWHh6t+\n/foXnT8wMFCff/65Tp8+raCgIOv0/fv3a+vWrQoMDHR1kT1i4sSJat++fZnpNXV7d+zYoVmzZmnI\nkCEVfudATUAYAiT17NlTS5Ys0ahRo0zz7LuCggIFBAQ4tYw//vhDknTvvfc6NCp8jx499O2332rt\n2rU2D1pMTU1V8+bN1axZMxUXFztVJm/UunVrhYeHe7oYAMrhs81kS5cuVZ8+fdS+fXvFxcVp27Zt\nni4SvNiYMWMkSe+8885F5yttvrrQhc0x+/btU2hoqJKSkvTGG2+oR48eioiI0NNPP60zZ87ozz//\n1AMPPKCIiAhFR0dr+fLldteXmZmpkSNHqmPHjurZs6fefvttlZSU2MyTk5OjyZMnKyoqSmFhYerf\nv7+Sk5Nt5iltfti6dasee+wxXX/99br99tsvuq3ffPONhg0bpg4dOqhz584aO3asNfxI55piSpsQ\n27Ztq9DQUKWkpFx0mbVr11ZMTIxSU1NtpqempuqWW26RxWIp856srCyNHz9ekZGRCgsL0+DBg8u8\nX5K+++47xcbGqn379rrpppv04Ycf2i3DmTNn9Nprr6lPnz4KCwtTnz599M4775T5XC+0YcMGDR8+\nXJ07d1ZERIRiYmI0a9asi77HEUeOHNF1112nDz74oMxr8+fPV7t27ZSTkyNJ2rhxo0aPHq2ePXuq\nY8eOGjRokBYuXFhhgHR0v5WkGTNmKDY2Vp06dVJkZKTuuecebd++3fp6SkqKnnvuOUlSv379rE1+\npU1bRUVFmjt3rvr376+wsDD17NlT06dP19mzZ23Ws3fvXj300EPq2LGjunXrpqlTp6qgoMCBTwy+\naOvWrXr44YcVFRVl91hiGIZmzpypnj17qkOHDho5cqT++9//2sxz4sQJPfPMM+rcubM6d+6sZ555\nxuGmaJ+sGVq1apWmTZumKVOmqHPnzvr3v/+t0aNH67PPPtOVV17p6eLBCwUHB+uuu+7S+++/r/vv\nv1/NmzevluXOmzdPXbt21fTp05WZmanXXntNfn5+2rFjh26//Xbdf//9SkpK0nPPPaewsDD99a9/\ntXn/I488oltvvVXx8fHauHGj5syZIz8/P40bN06SdPr0aY0YMUJnz57VuHHj1KJFC23YsEEvvvii\nCgoKrI/KKfX000/r5ptv1owZMy7aH+mbb75RfHy8unXrprfeekt5eXmaMWOG7rzzTqWmpqpp06aa\nNWuWFi9erJSUFGv4uvrqqyv8TIYOHapRo0bp0KFDuuKKK7R9+3bt3r1bQ4cO1datW23mzcvL08iR\nI3XixAk9+eSTuuKKK/Tpp59q/Pjxys/P17BhwySdC42jR49WWFiY3nrrLRUUFGjmzJnKy8uTv7+/\ndXlFRUV64IEHlJmZqTFjxig0NFTbt2/XnDlzdOLEiXL72ezdu1djxoxRTEyMxo4dq1q1aunPP//U\n3r17K9xe6Vzfqgs/b4vFIn9/fwUHB6t79+769NNPdc8999jM8+mnnyoqKkoNGza0lqN79+66++67\nVbt2bf3888+aOXOmcnJyqq2J9/Dhw7r33nt1xRVX6MyZM/r00091991365NPPlFoaKh69eqlMWPG\n6J133tHbb7+tK664QpLUpEkTSdIzzzyj9evX68EHH1SnTp2UmZmpt99+W/v377eG54KCAt13333K\nz8/X5MmT1ahRI3344Ydat25dtWwDap68vDyFhIRo6NChevbZZ8u8Pn/+fC1cuFDTp09Xy5YtNXv2\nbN13331avXq1tcn9qaee0sGDB7VgwQJJ55qnx48fr3fffbfiAhg+6LbbbjNeeOEFm2nR0dHG66+/\n7qESwVt98sknRkhIiLF7927j2LFjRufOnY0JEyYYhmEYhYWFRkhIiDFjxgzr/DNmzDBCQkLKLOfZ\nZ581evfubf3/3r17jZCQEGPkyJE28z3yyCNGSEiIsWLFCuu048ePG23btjVmzpxZZj1z5861ef8L\nL7xghIeHGydOnDAMwzBmzZplhIWFGbt27SozX9euXY3CwkKb7Xz55Zcd+lxiY2ON6Oho6/sNwzD2\n7NljXHfddca0adOs09588027n4c9vXv3Np566imjpKTE6N27t3XbpkyZYgwbNswwDMO4++67jeHD\nh1vfs3jxYiMkJMT4/vvvbZZ17733Gt26dTOKiooMwzCMJ5980ujatauRm5trnefAgQNGu3btbL6X\n5cuXGyEhIcaWLVtsljdnzhyjXbt2RnZ2tmEYhvH999/brDc9Pd0ICQkxTp065dC2lipdjr2/m2++\n2TpfamqqERISYmRmZlqn/frrr0ZISIjx2Wef2V12SUmJUVhYaMyZM8e4/vrrjeLiYutrvXv3Np59\n9lnr/x3dby9UVFRkFBYWGv369TMSExOt08//3Zxv69atRkhIiLF8+XKb6aXb9+uvvxqGYRjJyclG\nSEiIkZGRYZ2nuLjYGDhwoBESEmLs3bu33DLB94WHhxuffPKJ9f8lJSVGjx49jDlz5linnTlzxggP\nDzeSkpIMwzCMnTt3GiEhIca2bdus85Tuj+f/rsrjc81kBQUF+uWXX9SjRw+b6T169FBGRoaHSoWa\noH79+rrvvvuUmppq0xzkjBtuuMHm/61atZIkRUVFWaddfvnlatiwoQ4ePFjm/QMGDLD5/80336y8\nvDz9/vvvks413XTs2FEtWrRQUVGR9a9nz546fvy4du7cafP+6OjoCsucl5enX3/9VQMGDNAll/yv\n8viqq65Sp06dytTeVJbFYrE2dRUUFCg9PV1Dhw61O+/WrVvVtGlTRUZG2kwfMmSIcnJyrNu3fft2\n3Xjjjapbt651nmbNmikiIsLmfRs2bFDz5s0VERFh83n16NFDhYWFNs1B52vbtq1q1aqlJ554QqtX\nr9bRo0crtc2TJ0/WsmXLbP7eeust6+vR0dGqW7euTfNfamqq6tWrp759+1qnZWVlafLkyerdu7fC\nwsLUrl07/fOf/9TJkycrXabybNq0SSNHjlRkZKSuu+46tWvXTrt379auXbsqfO+GDRtUq1YtxcTE\nlNkfJVn3nYyMDDVr1symH5Wfn1+Z/R2QznU7OHLkiM15vU6dOurSpYv1vJ6RkaG6deuqU6dO1nk6\nd+6sunXrOnTu97lmsmPHjqm4uLhMJ9hGjRpp06ZNHioVaopRo0ZpyZIlmjFjhl5//XWnl3f55Zfb\n/L9WrVqSpMsuu8xmekBAQJk+FdK5/dbe/7OysiSd6y/0559/ql27dnbXf+Gtz8HBwRWW+eTJkzIM\nw9rscb7GjRtr//79FS6jIkOHDtW7776r2bNnKy8vTwMHDrQ734kTJ+yWufT3XTocwpEjR8p8VvbK\nm5OTo/379zv8eZW65pprtGDBAs2fP1/jx49XQUGBOnTooKefflpdu3a9+MZKatmypd27yUpdeuml\niomJ0cqVK/X444+rpKREaWlp6t+/v2rXri3pXFPbmDFjlJWVpXHjxqlVq1aqXbu2Pv/8c7377rt2\n95/K+uWXX/TQQw+pZ8+eevnllxUcHCw/Pz9NnDjRof48R48eVWFhYbmdxUs/3/K+L3vTgCNHjkiS\n3fN66bEwOztbDRs2tOl3aLFY1LBhQ2VnZ1e4Dp8LQ4AzAgMDFR8fr+nTp+uBBx4o83rpienCO7Gq\ne7yVUkePHrWp7Si9+i8NKvXr11fDhg31wgsv2H1/y5Ytbf5vr4PyhS677DJZLBbrAeh82dnZ1XIr\ndcuWLdWxY0fNmzdP0dHRZcJhqcsvv9xujUTpwa00bAYHB9utGbnwIFi/fn21aNFC//znP+2u72J9\nxbp166Zu3bqpoKBAP/zwg2bMmKH4+Hh98cUX1j49zrjlllu0fPly/fDDD8rPz9eRI0d0yy23WF/f\ns2ePfv75Z7366qs209evX1/hsh3db9euXSt/f3/NnDnTGtylcwG5vO/ofPXr11ft2rW1dOlSu6+X\n7rfBwcFlai0lVVvtFlBZPtdM1qBBA/n7+5c5CB49etShq2LgzjvvVNOmTe2eMEs74J9/F8PJkydd\n1gSbnp5u8//PPvtMdevWtd4ZFBUVpV27dunKK69U+/bty/ydP5aPo+rWrat27dpp9erVNncp7d+/\nXxkZGQ7VhDjiwQcfVO/evXX33XeXO0/Xrl116NAh/fDDDzbT09LS1KhRI1177bWSpPDwcH399dfK\ny8uzznPw4MEy30tUVJQOHTqkunXr2v28HAk1AQEB6t69ux588EHl5eVV2wCBkZGRuuKKK5Sammod\nauD666+3vp6fny9JNiGlsLBQK1eurHDZju63Z86ckZ+fn01o/u6773TgwAGb+UoDVWmZSkVFRens\n2bM6ffq03c+3adOmkqSIiAgdPHjQplmypKSkzP4OSP+r0bZ3Xi+tLWrcuLFycnJkGIb1dcMwlJOT\n49BwKT5XMxQQEKB27dpp06ZNNu3PmzZtUr9+/TxYMtQUAQEBeuSRRzRp0qQyr91www2qV6+eJk2a\npHHjxqmgoEALFiywqb2pTh999JFKSkrUvn17bdy4UR9//LHGjRunevXqSTrXrLdq1SrdeeedGjVq\nlFq2bKkzZ87ojz/+0LZt2yocKqA8f//73xUfH6/4+HjdeeedysvL08yZMxUUFKT77ruvWratX79+\nFf4mY2Nj9cEHH2jcuHF64okn1LRpU61cuVLffvutEhISrHeKjR07VmvWrNH999+vBx98UAUFBZo1\na1aZZpfBgwcrJSVFo0aN0v333682bdqooKBAe/fu1ZdffqnZs2fr0ksvLVOOpKQkbdu2TTfccIOa\nNWumY8eOae7cuWrSpIlCQkIq3NbMzEy7+0hISIh1up+fnwYPHqzk5GQVFRXp3nvvtQklrVq1UvPm\nzfXWW2/Jz89Pl1xyid5///0K1y05vt9GRUXp/fff14QJE3Trrbdq165dmjNnjjXElCoNoUuXLlVs\nbKwuueQShYaGKjIyUoMGDdJjjz2mUaNGqUOHDvLz89P+/fv19ddf6+mnn1bLli01dOhQzZs3T48+\n+qiefPJJNWrUSElJSTp9+rRD2wNzadGihYKDg7Vp0yZ16NBBknT27Flt27ZN48ePl3QuYOfl5Skj\nI8PabygjI0N5eXll+g7a43NhSJLuu+8+jR8/Xh06dFCnTp2UlJSkrKwsDR8+3NNFQw0RFxen9957\nT7t377aZftlll+ndd9/VK6+8oscff1xXXHGFxo4dq++++05btmyp9nLMmTNHiYmJmjNnjurVq6cx\nY8bYPPKiXr16+vDDDzV79mzNnz9fWVlZqlevnlq2bOlU+L/hhhs0d+5czZ49W48//rhq1aqlrl27\n6plnnilzYnSlunXramXm35QAAAIdSURBVPHixXrttdf0+uuvKzc3Vy1btizTVNS6dWvNmzdPr776\nqh5//HE1bdpUo0eP1vbt222+l1q1aum9997TvHnzlJycrH379qlu3bq66qqr1KtXL5tal/O1adNG\n33zzjd58800dPXpU9evXV6dOnfT666+rTp06FW7H1KlT7U5ftmyZTV+iW265RfPnz7f++3wBAQGa\nPXu2EhIS9Oyzz+ryyy/XrbfeqiuvvFITJ0686Pod3W+joqI0ceJELVq0SGvXrtVf//pXvfrqq2VC\ndZs2bTRu3DglJyfr448/VklJib744gu1aNFCr732mhYvXqxPPvlE7777rgICAtS8eXP17NnTeoUe\nEBCgRYsWKSEhQS+99JIuvfRSDRo0SL169dKUKVMq/Dzhe3Jzc7Vnzx5J52oJDxw4oB07dujyyy/X\nlVdeqXvuuUdz585Vq1at9Je//EXvvPOO6tatq0GDBkk6dwyIiorSlClTlJCQIEmaMmWKevfubb1x\n5WIsxvl1Sj5k6dKleu+995SVlaWQkBA999xz6tKli6eLBQAALrB58+Yy42xJ52qHp0+fLsMwNGvW\nLCUnJ+vEiRPq2LGjJk+ebFMze+LECSUmJurLL7+UdG5g2MmTJzvU381nwxAAAIAjfK4DNQAAQGUQ\nhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKkRhgAAgKn9f7dL/S6E7Ku4\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcbfqOwawm6q",
        "colab_type": "text"
      },
      "source": [
        "**Progressive Dynamic Hurdles**\n",
        "\n",
        "Finally, we run Progressive Dynamic Hurdles (PDH). By establishing a hurdle, PDH is able to utilize early observations to filter out flagrantly bad models, training only the most promising models for the high-cost maximum number of train steps. This perfect signal clarifies which of these promising models are truly the best and improves the search by generating candidates from the best parents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EIMYIXpziE-",
        "colab_type": "code",
        "outputId": "9acc0929-3c04-46f7-d355-f8ae16d977b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "history, _ = pdh_evolution(train_resources=TOTAL_RESOURCES,\n",
        "                           population_size=POPULATION_SIZE,\n",
        "                           sample_size=SAMPLE_SIZE)\n",
        "\n",
        "graph_history(history)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NG5syZQoyMzMxd+5cbNiwAREREQgKCkJWVhYyMjJw7NgxfPrpp4Zn7/Tu3Rvff/89du7c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\n08EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CwkJER0dj8eLFxuyN5duYw8PDsWLFCsycORMDBw5E7dq18eCDD5oNfS8rK8N7772H06dP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\n4BqEDwAAcA3CBwAAuAbhAwAAXOMnffPZN/jXhkMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIA_b2vNNkhX",
        "colab_type": "text"
      },
      "source": [
        "**Mean Fitness Comparison**\n",
        "\n",
        "To demonstrate the effectiveness of Progressive Dynamic Hurdles, we compare the mean top fitness of each algorithm, with each run 500 times."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jix43gRkF-TQ",
        "colab_type": "code",
        "outputId": "6009d9b0-6108-44ab-cc83-f0ac406ff193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "num_trials = 500\n",
        "\n",
        "print(\"===========================\")\n",
        "print(\"Mean Top Fitness Comparison\")\n",
        "print(\"===========================\")\n",
        "\n",
        "max_fitnesses = []\n",
        "for _ in range(num_trials):\n",
        "  _, population = plain_evolution(\n",
        "      cycles=TOTAL_RESOURCES, population_size=POPULATION_SIZE,\n",
        "      sample_size=SAMPLE_SIZE,\n",
        "      early_observation=True)\n",
        "  # Assume all models in the final population are fully evaluated\n",
        "  max_fitness = max([indiv.true_accuracy for indiv in population])\n",
        "  max_fitnesses.append(max_fitness)\n",
        "max_fitnesses = np.array(max_fitnesses)\n",
        "print(\"Early Observation Plain Evolution: %.4s ± %.4s\" %\n",
        "      (np.mean(max_fitnesses), np.std(max_fitnesses)))\n",
        "  \n",
        "max_fitnesses = []\n",
        "for _ in range(num_trials):\n",
        "  _, population = plain_evolution(\n",
        "    cycles=TOTAL_RESOURCES/REDUCTION_FACTOR, population_size=POPULATION_SIZE,\n",
        "    sample_size=SAMPLE_SIZE,\n",
        "    early_observation=False)\n",
        "  # Assume all models in the final population are fully evaluated\n",
        "  max_fitness = max([indiv.true_accuracy for indiv in population])\n",
        "  max_fitnesses.append(max_fitness)\n",
        "max_fitnesses = np.array(max_fitnesses)\n",
        "print(\"Max Step Observation Plain Evolution: %.4s ± %.4s\" %\n",
        "      (np.mean(max_fitnesses), np.std(max_fitnesses)))\n",
        "\n",
        "max_fitnesses = []\n",
        "for _ in range(num_trials):\n",
        "  _, population = pdh_evolution(train_resources=TOTAL_RESOURCES,\n",
        "                                population_size=POPULATION_SIZE,\n",
        "                                sample_size=SAMPLE_SIZE)\n",
        "  # Assume all models in the final population are fully evaluated\n",
        "  max_fitness = max([indiv.true_accuracy for indiv in population])\n",
        "  max_fitnesses.append(max_fitness)\n",
        "max_fitnesses = np.array(max_fitnesses)\n",
        "print(\"Progressive Dynamic Hurdles: %.4s ± %.4s\" %\n",
        "      (np.mean(max_fitnesses), np.std(max_fitnesses)))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===========================\n",
            "Mean Top Fitness Comparison\n",
            "===========================\n",
            "Early Observation Plain Evolution: 0.60 ± 0.03\n",
            "Max Step Observation Plain Evolution: 0.62 ± 0.02\n",
            "Progressive Dynamic Hurdles: 0.64 ± 0.03\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}